Physics-Informed Neural Networks (PINNs) for Sound Field Predictions with Parameterized Sources and Impedance Boundaries

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PubDate: Sep 2021

Teams: University of Denmark

Writers: Nikolas Borrel-Jensen, Allan P. Engsig-Karup, Cheol-Ho Jeong

PDF: Physics-Informed Neural Networks (PINNs) for Sound Field Predictions with Parameterized Sources and Impedance Boundaries

Abstract

Realistic sound is essential in virtual environments, such as computer games and mixed reality. Efficient and accurate numerical methods for pre-calculating acoustics have been developed over the last decade, however, pre-calculating acoustics makes handling dynamic scenes with moving sources challenging and requires intractable memory storage. A Physics-Informed Neural Networks (PINNS) method is presented, learning a compact and efficient surrogate model with parameterized moving sources and impedance boundaries, satisfying a system of coupled equations. The trained model shows relative mean errors below 2%/0.2 dB, indicating that acoustics with moving sources and impedance boundaries can be predicted in real-time using PINNs.

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